CS177 Review/Summary of the Madej lectures

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CS177 Review/Summary of the Madej lectures

• Tom Madej 12.07.05

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Overview

• Basic biology.
• Protein/DNA sequence comparison.
• Protein structure comparison/classification.
• NCBI databases overview.
• Miscellaneous topics.

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Lodish et al. Molecular Cell Biology, W.H.
Freeman 2000
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Protein/DNA sequence comparison

• What is the meaning of a sequence alignment?
• Scoring methods amino acid substitution
  matrices, PSSMs.
• Basic computational methods e.g. BLAST.
• Know how to run PSI-BLAST, interpret the results.

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Homology

• whenever statistically significant sequence or
  structural similarity between proteins or protein
  domains is observed, this is an indication of
  their divergent evolution from a common ancestor
  or, in other words, evidence of homology.
• E.V. Koonin and M.Y. Galperin, Sequence
  Evolution Function, Kluwer 2003

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A simple phylogenetic tree
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Human hemoglobin and more distantly related
globins

• Human and horse
• Human and fish
• Human and insect
• Human and bacteria

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Alignment notation different notations for the
same alignment!
VISDWNMPN-------MDGLE CILVV----AANDGPMPQTRE
VISDWnm---pnMDGLE CILVVaandgpmPQTRE
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Computing sequence alignments

• You must be able to recognize the answer
  (correct alignment) when you see it (scoring
  system).
• You must be able to find the answer i.e. compute
  it efficiently.

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Scoring and computing alignments

• Position independent amino acid substitution
  tables e.g. BLOSUM62.
• Global alignment algorithms such as
  Smith-Waterman (dynamic programming) or fast
  heuristics such as BLAST.

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Score this alignment
VISDWnm---pnMDGLE CILVVaandgpmPQTRE
Use BLOSUM62 matrix gap opening penalty 10 gap
extension penalty 1
(-1 4 2 3 3) 10 111 (-2 0 2 2
5) -27
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BLAST (Basic Local Alignment Search Tool)

• Extremely fast, can be on the order of 50-100
  times faster than Smith-Waterman.
• Method of choice for database searches.
• Statistical theory for significance of results
  (extreme value distribution).
• Heuristic does not guarantee optimal results.
• Many variants, e.g. PHI-, PSI-, RPS-BLAST.

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Why database searches?

• Gene finding.
• Assigning likely function to a gene.
• Identifying regulatory elements.
• Understanding genome evolution.
• Assisting in sequence assembly.
• Finding relations between genes.

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Issues in database searches

• Speed.
• Relevance of the search results (selectivity).
• Recovering all information of interest
  (sensitivity).
• The results depend on the search parameters, e.g.
  gap penalty, scoring matrix.
• Sometimes searches with more than one matrix
  should be performed.

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E-values, P-values

• E-value, Expectation value this is the expected
  number of hits of at least the given score, that
  you would expect by random chance for the search
  database.
• P-value, Probability value this is the
  probability that a hit would attain at least the
  given score, by random chance for the search
  database.
• E-values are easier to interpret than P-values.
• If the E-value is small enough, e.g. no more than
  0.10, then it is essentially a P-value.

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PSI-BLAST

• Position Specific Iterated BLAST
• As a first step runs a (regular) BLAST.
• Hits that cross the threshold are used to
  construct a position specific score matrix
  (PSSM).
• A new search is done using the PSSM to find more
  remotely related sequences.
• The last two steps are iterated until convergence.

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PSSM (Position Specific Score Matrix)

• One column per residue in the query sequence.
• Per-column residue frequencies are computed so
  that log-odds scores may be assigned to each
  residue type in each column.
• There are difficulties e.g. pseudo-counts are
  needed if there are not a lot of sequences, the
  sequences must be weighted to compensate for
  redundancy.

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Two key advantages of PSSMs

• More sensitive scoring because of improved
  estimates of probabilities for a.a.s at specific
  positions.
• Describes the important motifs that occur in the
  protein family and therefore enhances the
  selectivity.

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Position Specific Substitution Rates
Weakly conserved serine
Active site serine
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Position Specific Score Matrix (PSSM)
A R N D C Q E G H I L K M
F P S T W Y V 206 D 0 -2 0 2 -4 2 4
-4 -3 -5 -4 0 -2 -6 1 0 -1 -6 -4 -1 207 G
-2 -1 0 -2 -4 -3 -3 6 -4 -5 -5 0 -2 -3 -2 -2
-1 0 -6 -5 208 V -1 1 -3 -3 -5 -1 -2 6 -1
-4 -5 1 -5 -6 -4 0 -2 -6 -4 -2 209 I -3 3
-3 -4 -6 0 -1 -4 -1 2 -4 6 -2 -5 -5 -3 0 -1
-4 0 210 D -2 -5 0 8 -5 -3 -2 -1 -4 -7 -6
-4 -6 -7 -5 1 -3 -7 -5 -6 211 S 4 -4 -4 -4
-4 -1 -4 -2 -3 -3 -5 -4 -4 -5 -1 4 3 -6 -5 -3
212 C -4 -7 -6 -7 12 -7 -7 -5 -6 -5 -5 -7 -5 0
-7 -4 -4 -5 0 -4 213 N -2 0 2 -1 -6 7 0
-2 0 -6 -4 2 0 -2 -5 -1 -3 -3 -4 -3 214 G
-2 -3 -3 -4 -4 -4 -5 7 -4 -7 -7 -5 -4 -4 -6 -3
-5 -6 -6 -6 215 D -5 -5 -2 9 -7 -4 -1 -5 -5
-7 -7 -4 -7 -7 -5 -4 -4 -8 -7 -7 216 S -2 -4
-2 -4 -4 -3 -3 -3 -4 -6 -6 -3 -5 -6 -4 7 -2 -6
-5 -5 217 G -3 -6 -4 -5 -6 -5 -6 8 -6 -8 -7
-5 -6 -7 -6 -4 -5 -6 -7 -7 218 G -3 -6 -4 -5
-6 -5 -6 8 -6 -7 -7 -5 -6 -7 -6 -2 -4 -6 -7 -7
219 P -2 -6 -6 -5 -6 -5 -5 -6 -6 -6 -7 -4 -6 -7
9 -4 -4 -7 -7 -6 220 L -4 -6 -7 -7 -5 -5 -6
-7 0 -1 6 -6 1 0 -6 -6 -5 -5 -4 0 221 N
-1 -6 0 -6 -4 -4 -6 -6 -1 3 0 -5 4 -3 -6 -2
-1 -6 -1 6 222 C 0 -4 -5 -5 10 -2 -5 -5 1
-1 -1 -5 0 -1 -4 -1 0 -5 0 0 223 Q 0 1
4 2 -5 2 0 0 0 -4 -2 1 0 0 0 -1 -1 -3 -3
-4 224 A -1 -1 1 3 -4 -1 1 4 -3 -4 -3 -1
-2 -2 -3 0 -2 -2 -2 -3
Serine scored differently in these two positions
Active site nucleophile
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PSI-BLAST key points

• The first PSSM is constructed from all hits that
  cross the significance threshold using standard
  BLAST.
• The search is then carried out with the PSSM to
  draw in new significant hits.
• If new hits are found then a new PSSM is
  constructed these last two steps are iterated.
• The computation terminates upon convergence,
  i.e. when no new sequences are found to cross the
  significance threshold.

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Protein structure comparison/classification

• Protein secondary structure elements.
• Supersecondary structures (simple structure
  motifs).
• Folds and domains.
• Comparing structures (VAST).
• Superfolds.
• Fold classification (SCOP).
• Conserved Domain Database (CDD).

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a-helix (3chy)
backbone atoms
with sidechains
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Parallel ß-strands (3chy)
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Anti-parallel ß-strands (1hbq)
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Higher level organization

• A single protein may consist of multiple domains.
  Examples 1liy A, 1bgc A. The domains may or
  may not perform different functions.
• Proteins may form higher-level assemblies.
  Useful for complicated biochemical processes that
  require several steps, e.g. processing/synthesis
  of a molecule. Example 1l1o chains A, B, C.

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Supersecondary structures

• ß-hairpin
• a-hairpin
• ßaß-unit
• ß4 Greek key
• ßa Greek key

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Supersecondary structure simple units
G.M. Salem et al. J. Mol. Biol. (1999) 287 969-981
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Supersecondary structure Greek key motifs
G.M. Salem et al. J. Mol. Biol. (1999) 287 969-981
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Protein folds

• There is a continuum of similarity!
• Fold definition two folds are similar if they
  have a similar arrangement of SSEs (architecture)
  and connectivity (topology). Sometimes a few
  SSEs may be missing.
• Fold classification To get an idea of the
  variety of different folds, one must adjust for
  sequence redundancy and also try to correctly
  assign homologs that have low sequence identity
  (e.g. below 25).

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Vector Alignment Search Tool (VAST)

• Fast structure comparison based on representing
  SSEs by vectors.
• A measure of statistical significance (VAST
  E-value) is computed (very differently from a
  BLAST E-value).
• VAST structure neighbor lists useful for
  recognizing structural similarity.

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Superfolds (Orengo, Jones, Thornton)

• Distribution of fold types is highly non-uniform.
• There are about 10 types of folds, the
  superfolds, to which about 30 of the other folds
  are similar. There are many examples of
  isolated fold types.
• Superfolds are characterized by a wide range of
  sequence diversity and spanning a range of
  non-similar functions.
• It is a research question as to the evolutionary
  relationships of the superfolds, i.e. do they
  arise by divergent or convergent evolution?

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Superfolds and examples

• Globin 1hlm sea cucumber hemoglobin 1cpcA
  phycocyanin 1colA colicin
• a-up-down 2hmqA hemerythrin 256bA cytochrome
  B562 1lpe apolipoprotein E3
• Trefoil 1i1b interleukin-1ß 1aaiB ricin 1tie
  erythrina trypsin inhibitor
• TIM barrel 1timA triosephosphate isomerase 1ald
  aldolase 5rubA rubisco
• OB fold 1quqA replication protein A 32kDa
  subunit 1mjc major cold-shock protein 1bcpD
  pertussis toxin S5 subunit

• a/ß doubly-wound 5p21 Ras p21 4fxn flavodoxin
  3chy CheY
• Immunoglobulin 2rhe Bence-Jones protein 2cd4
  CD4 1ten tenascin
• UB aß roll 1ubq ubiquitin 1fxiA ferredoxin 1pgx
  protein G
• Jelly roll 2stv tobacco necrosis virus 1tnfA
  tumor necrosis factor 2ltnA pea lectin
• Plaitfold (Split aß sandwich) 1aps
  acylphosphatase 1fxd ferredoxin 2hpr
  histidine-containing phosphocarrier

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Fold classification (when you have the structure)

• First, look up PubMed abstracts for any relevant
  papers. E.g. if this is from a PDB file there
  will be references in it.
• Try checking SCOP or CATH.
• Look at VAST neighbors. See if the structure in
  question is highly similar to another structure
  with a known fold.

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SCOP (Structural Classification of Proteins)

• http//scop.mrc-lmb.cam.ac.uk/scop/
• Levels of the SCOP hierarchy
• Family clear evolutionary relationship
• Superfamily probable common evolutionary origin
• Fold major structural similarity

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Bioinformatics databases

• Entrez is by far the most useful, because of the
  links between the individual databases, e.g.
  literature, sequence, structure, taxonomy, etc.
• Other specialty databases available on the
  internet can also be very useful, of course!

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The (ever expanding) Entrez System
NLM Catalog
PubChem
Compounds
BioAssays
Substances
Literature
Organism
Expression
HomoloGene
Gene
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Links Between and Within Nodes
Word weight
Computational
3-D Structure
3 -D Structures
VAST
Phylogeny
Computational
Protein sequences
BLAST
BLAST
Computational
Computational
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Entrez queries

• Be able to formulate queries using index terms
  (Preview/Index), and limits.

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Exercises!

• How many protein structures are there that
  include DNA and are from bacteria?
• In PubMed, how many articles are there from the
  journal Science and have Alzheimer in the title
  or abstract, and amyloid beta anywhere? How
  many since the year 2000?
• Notice that the results are not 100 accurate!
• In 3D Domains, how many domains are there with no
  more than two helices and 8 to 10 strands and are
  from the mouse?

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P53 tumor suppressor protein

• Li-Fraumeni syndrome only one functional copy of
  p53 predisposes to cancer.
• Mutations in p53 are found in most tumor types.
• p53 binds to DNA and stimulates another gene to
  produce p21, which binds to another protein cdk2.
  This prevents the cell from progressing thru the
  cell cycle.

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G. Giglia-Mari, A. Sarasi, Hum. Mutat. (2003) 21
217-228.
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Exercise!

• Use Cn3D to investigate the binding of p53 to
  DNA.
• Formulate a query for Structure that will require
  the DNA molecules to be present (there are 2
  structures like this).

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Miscellaneous topics

• BLAST a sequence against a genome locate hits on
  chromosomes with map viewer.
• Obtain genomic sequence with map viewer.
• Spidey to predict intron/exon structure (but we
  wont use spidey on the exam!).
• How sequence variations can affect protein
  structure/function.

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EST exercise summary

• BLAST the EST (or other DNA seq) against the
  genome.
• From the BLAST output you can get the genomic
  coordinates of any nucleotide differences.
• Use map viewer to locate the hit on a chromosome
  assume the hit is in the region of a gene.
• By following the gene link you can get an
  accession for mRNA.
• By using the dl link you can get an accession
  for the genomic sequence.
• Use spidey with the mRNA and genomic sequence
  to locate changed residues in the protein.

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EST exercise summary (cont.)

• From the gene report you can follow the protein
  link, and then Blink.
• From the BLAST link page you can get to CDD and
  related structures.
• Since you know where are the changed residues you
  can use the structures to study what effect the
  changes might have on the function of the protein.

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Gene variants that can affect protein function

• Mutation to a stop codon truncates the protein
  product!
• Insertion/deletion of multiple bases changes the
  sequence of amino acid residues.
• Single point change could alter folding
  properties of the protein.
• Single point change could affect the active site
  of the protein.
• Single point change could affect an interaction
  site with another molecule.

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Important note!

• Most diseases (e.g. cancer) are complex and
  involve multiple factors (not just a single
  malfunctioning protein!).

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Investigating a genetic disease

• The following EST comes from a hemochromatosis
  patient your task is to identify the gene and
  specific mutation causing the illness, and why
  the protein is not functioning properly.
• The sequence
• TGCCTCCTTTGGTGAAGGTGACACATCATGTGACCTCTTCAG
• TGACCACTCTACGGTGTCGGGCCTTGAACTACTACCCCCAGA
• ACATCACCATGAAGTGGCTGAAGGATAAGCAGCCAATGGAT
• GCCAAGGAGTTCGAACCTAAAGACGTATTGCCCAATGGGGA
• TGGGACCTACCAGGGCTGGATAACCTTGGCTGTACCCCCTGG
• GGAAGAGCAGAGATATACGTACCAGGTGGAGCACCCAGGCC
• TGGATCAGCCCCTCATTGTGATCTGGG

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ESTs

• Expressed Sequence Tags useful for discovering
  genes, obtaining data on gene expression/regulatio
  n, and in genome mapping.
• Short nucleotide sequences (200-500 bases or so)
  derived from mRNA expressed in cells.
• The introns from the genes will already be
  spliced out.
• mRNA is unstable, however, and so it is reverse
  transcribed into cDNA.

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Hemochromatosis 2

• BLAST the EST vs. the Human genome (could take a
  few minutes).
• - Which chromosome is hit?
• - What is the contig that is hit (reference
  assembly)?
• - Is the EST identical to the genomic sequence?
• - Take note of the coords of the difference.
• Click on Genome View.
• Select the map element at the bottom
  corresponding to the contig.

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Hemochromatosis 3

• What gene is hit? Zoom in on the BLAST hit a few
  times.
• Display the entire gene sequence vi dl and
  Display.
• Copy and save the genomic sequence.
• Record the coords for the start of the genomic
  sequence.

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Hemochromatosis 4

• Click on a UniGene link Hs.233325.
• Note Expression profile presents data for the
  expression level of the gene in various tissues.
• How many mRNAs and ESTs are there for the HFE
  gene?
• Take note of the mRNA accession NM_000410.

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Hemochromatosis 5

• Go to spidey http//www.ncbi.nlm.nih.gov/spidey
  /
• To determine the intron/exon structure, paste the
  HFE gene sequence into the upper box, and enter
  the HFE mRNA accession NM_000410 in the lower
  box.
• Click Align.

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Hemochromatosis 6

• How many exons are there?
• Which exon codes the residue that is changed in
  the original EST? (You have to do a little
  arithmetic!)
• Record some of the protein sequence around the
  changed residue EQRYTCQVEHPG

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Hemochromatosis 7

• From the Map Viewer page click on the HFE gene
  link.
• How many HFE transcripts are there? Which is the
  longest isoform?
• Follow Links to Protein and then to the
  report for NP_000410.
• Determine the residue number that corresponds to
  the mutation.

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RNA splicing and isoforms
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Hemochromatosis 8

• What effect does the mutation in the original EST
  have on the protein? (Look at the table for the
  Genetic Code.)
• Go back to the Gene Report read the summary and
  take note of the GeneRIF bibliography.
• Now go to Links and then to GeneView in dbSNP
  to a list of known SNPs.

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Hemochromatosis 9

• In the SNP list note that the one you want is
  currently shown.
• Select view rs in gene region and then click on
  view rs.
• How many nonsynonomous substitutions do you see?
• Do you see the one we are particularly interested
  in?

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Digression SNPs

• Single Nucleotide Polymorphisms.
• A single base change that can occur in a persons
  DNA.
• On average SNPs occur about 1 of the time, most
  are outside of protein coding regions.
• Some SNPs may cause a disease some may be
  associated with a disease others may affect
  disposition to a disease others may be simple
  genetic variation.
• dbSNP archives SNPs and other variations such as
  small-scale deletion/insertion polymorphisms
  (DIPs), etc.

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Hemochromatosis 10

• Back to the Gene Report, click on Links and go
  to OMIM (can also get there via the Map
  Viewer).
• In the OMIM entry you can read a bit also click
  on View List for Allelic Variants, where you
  can see the mutation again.

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Hemochromatosis 11

• From the Gene Report again follow Links to
  Protein and scroll down to NP_000401.
• Click on Domains and then Show Details.
• What is the Conserved Domain in the region of
  interest?
• Follow the link to the CD.
• Click on View 3D Structure.

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Hemochromatosis 12

• Look for residue position 282 in the query
  sequence.
• Highlight that column.
• Is the Cys282 conserved in the family?
• The C282Y mutation therefore likely has the
  effect of